backup: 2026-08-29 06:59

impldatasets.org

@@ -7,7 +7,53 @@
 
 * impl/datasets
 
-** KEEL Dataset Class Balance
+** Datasets with Final Results
+
+*** CIFAR-10N (image)
+- *Size*: 50,000 training images (32×32 RGB, CIFAR-10 base)
+- *Noise*: clean 0% / aggre ~9% / worse ~40% (human annotator noise)
+- *Source*: UCSC-REAL/cifar-10-100n — standard noisy-label benchmark (Learning with Noisy Labels, NeurIPS 2021)
+- *Notes*: Positive class = automobile (class 1) vs rest; natural IR ~9:1. Three noise conditions all have final results. Labels replaced with real human annotation noise from crowdworkers.
+
+*** Credit Card Fraud (tabular)
+- *Size*: 284,807 transactions
+- *Noise*: 0% (clean; real confirmed fraud labels)
+- *Source*: Kaggle / ULB (Worldline + MLG research collaboration, 2013)
+- *Notes*: Extreme imbalance — only 492 frauds (IR ~577:1). 30 features: V1–V28 are PCA-transformed (original features confidential), plus Time and Amount. Time/Amount rescaled; V1–V28 left as-is.
+
+*** Bank Marketing (tabular)
+- *Size*: 41,188 samples; ~48 features after one-hot encoding
+- *Noise*: 0% (clean)
+- *Source*: UCI Machine Learning Repository (Moro et al., 2014 — Portuguese bank direct marketing campaigns)
+- *Notes*: IR ~8:1 (~11.3% positive). Target: whether client subscribed to term deposit. Mix of demographic, contact, and economic features.
+
+*** Home Credit (tabular)
+- *Size*: 307,511 loan applications; ~120 numerical features
+- *Noise*: 0% (clean; proxy labels — loan repayment used as credit default indicator)
+- *Source*: Kaggle Home Credit Default Risk competition (2018)
+- *Notes*: IR ~11.4:1 (~8% default rate). Features from application_train.csv only (no auxiliary tables). Non-numeric and ID columns dropped; NaNs filled with 0.
+
+*** KEEL Datasets (tabular, 12 with final results)
+- *Noise*: 0% (clean — standard imbalanced learning benchmarks)
+- *Source*: KEEL repository (Knowledge Extraction based on Evolutionary Learning)
+- *Notes*: All small/medium tabular datasets used as standard IR benchmarks in the literature. Loaded via keel_ds Python package.
+
+| Dataset      | Total | Pos | Neg | IR   | Notes                              |
+|--------------+-------+-----+-----+------+------------------------------------|
+| glass0       |   214 |  70 | 144 |  2.1 | Glass identification (UCI)         |
+| glass1       |   214 |  76 | 138 |  1.8 | Glass identification (UCI)         |
+| glass2       |   214 |  17 | 197 | 11.6 | Glass identification (UCI)         |
+| glass6       |   214 |  29 | 185 |  6.4 | Glass identification (UCI)         |
+| haberman     |   306 |  81 | 225 |  2.8 | Breast cancer survival (1958–1970) |
+| iris0        |   150 |  50 | 100 |  2.0 | Iris setosa vs others              |
+| new-thyroid1 |   215 |  35 | 180 |  5.1 | Thyroid function classification    |
+| pima         |   768 | 268 | 500 |  1.9 | Pima Indian diabetes (UCI)         |
+| vehicle2     |   846 | 218 | 628 |  2.9 | Vehicle silhouette (UCI)           |
+| vowel0       |   988 |  90 | 898 | 10.0 | Vowel recognition (class 0 vs rest)|
+| wisconsin    |   683 | 239 | 444 |  1.9 | Wisconsin breast cancer (UCI)      |
+| yeast3       |  1484 | 163 |1321 |  8.1 | Yeast protein localization (UCI)   |
+
+** KEEL Dataset Class Balance (full list)
 
 Counts from =KeelDataset= loader. Ratio = false/true (imbalance factor).
 Two datasets fail to load (likely missing env vars or files): =led7digit=, =page-blocks=.
@@ -38,3 +84,6 @@ Two datasets fail to load (likely missing env vars or files): =led7digit=, =page
 - ISJ "Need more data" failure: cleveland (13 true), ecoli (20 true), glass4 (13), glass5 (9) — after 33% test split, too few samples in minority class for ISJ root-finding. Excluded from =run_all.sh=.
 - glass2 (17 true) is borderline — watch for failures.
 - led7digit and page-blocks fail to load entirely (missing dataset files or env vars).
+- Credit card V1–V28 are pre-PCA (do NOT re-scale them); only Time (col 0) and Amount (col 29) are rescaled.
+- Home Credit uses application_train.csv only — no auxiliary tables (bureau, previous applications, etc.) merged in.
+- CIFAR-10N aggre noise rate is ~9%, worse is ~40% — large gap; both have completed final results.